Called the “Thomas Edison of our time,” Ray Kurzweil is a legendary inventor and futurist, whose many inventions include groundbreaking discoveries in speech technology. In a keynote speech to the crowd at the Gartner Business Intelligence (BI) Summit, he casually reminded audiences they have reason to pay attention: Of the 147 predictions he made about the year 2009 in the 1980s and 1990s, 86 percent of them came true. One that didn’t? Self-driving cars, which had driven plenty of miles in 2009 via Google’s tests, but were not yet available on the market. More on what self-driving cars have to do with BI later.

Kurzweil spoke of dazzling things—tiny robots that would replace red blood cells, making it possible for people to stay underwater for hours or run Olympic sprints. How 3D printing will create on-demand clothes, disrupting the retail market. How machine learning, like Watson, will become vastly more accurate and powerful. How the future of computer processing will be 3D, self-organizing molecular circuits. To understand and predict the future, Kurzweil uses what he calls the law of accelerating returns, which holds that information technology improves at an exponential rate.

In the near future, that means it’s important for businesses to understand how much faster, powerful, or cheaper their information technology will become over time. Even as computing capacity “deflates” nearly fifty percent every couple years, demand continues to rise, creating new opportunities and new areas to conquer. What was Big Data today will be trivial tomorrow. During a presentation, HortonWorks’ Will Hayes referred to “zettabytes of data” as part of the future. According to some estimates, the entire world wide web is just four zettabyes of data—at the moment. “Hadoop didn’t disrupt data,” Hayes told the audience. “New types ofdata disrupt the data center.”

One of those new types of data is sensor data, which will multiply as the Internet of Things becomes less of a buzzword and more of a reality. Already, machines send back information on thousands (or more) types of information. And sensor data is just one part of a huge data ecosystem, which sees companies combining information from internal CRM or ERP systems with external data about weather, census, and economic forecasts, along with other detailed, often unstructured information like weblogs from Internet browsing and social media data. Gartner’s W. Roy Schulte noted that ten years ago, banks would collect information on 60 attributes. Today, they collect over 400 attributes. Using Kurzweil’s law of accelerating returns, it appears there’s an exponential relationship at work—in ten years, could companies be collecting information on thousands of attributes?

So now to my final question: What do self-driving cars have to do with BI? Self-driving cars use machine learning in order to develop smarter techniques to travel the road. Machine learning is already in use today in the analytics world. Using Splunk, which helps companies analyze machine data, payroll and benefits administration company ADP was able to harness machine learning to evaluate the performance of their mobile application and realize new insights. For example, many people were clicking from their current pay statement to a previous one, in order to compare. ADP added a side-by-side comparison tool in order to respond to the user behavior. Machine learning is also behind hard-to-quantify potential data sources, like facial recognition and speech analysis, making it a both a future data source and a future way to perform analysis.

Self-driving cars epitomize the future of BI: one in which machines, not humans, categorize data and make decisions. BI started out merely descriptive. Then it became predictive, offering possible outcomes or assessing risk, like giving a potential credit card customer a score based on their history. The next step, and one that’s been heavily emphasized by Gartner researchers at the conference, is prescriptive analytics, in which computers would tell people what action to take based on analytics. And is that any different than trusting a car can drive you to your destination better than you?